Highly heritable resistance to root-lesion nematode (Pratylenchus thornei) in Australian chickpea germplasm observed using an optimised glasshouse method and multi-environment trial analysis

Pratylenchus thornei is a root-lesion nematode (RLN) of economic significance in the grain growing regions of Australia. Chickpea (Cicer arietinum) is a significant legume crop grown throughout these regions, but previous testing found most cultivars were susceptible to P. thornei. Therefore, improved resistance to P. thornei is an important objective of the Australian chickpea breeding program. A glasshouse method was developed to assess resistance of chickpea lines to P. thornei, which requires relatively low labour and resource input, and hence is suited to routine adoption within a breeding program. Using this method, good differentiation of chickpea cultivars for P. thornei resistance was measured after 12 weeks. Nematode multiplication was higher for all genotypes than the unplanted control, but of the 47 cultivars and breeding lines tested, 17 exhibited partial resistance, allowing less than two fold multiplication. The relative differences in resistance identified using this method were highly heritable (0.69) and were validated against P. thornei data from seven field trials using a multi-environment trial analysis. Genetic correlations for cultivar resistance between the glasshouse and six of the field trials were high (>0.73). These results demonstrate that resistance to P. thornei in chickpea is highly heritable and can be effectively selected in a limited set of environments. The improved resistance found in a number of the newer chickpea cultivars tested shows that some advances have been made in the P. thornei resistance of Australian chickpea cultivars, and that further targeted breeding and selection should provide incremental improvements.

Tree species diversity and ecosystem function: Can tropical multi-species plantations generate greater productivity?

Results from the humid tropics of Australia demonstrate that diverse plantations can achieve greater productivity than monocultures. We found that increases in both the observed species number and the effective species richness were significantly related to increased levels of productivity as measured by stand basal area or mean individual tree basal area. Four of five plantation species were more productive in mixtures with other species than in monocultures, offering on average, a 55% increase in mean tree basal area. A general linear model suggests that species richness had a significant effect on mean individual tree basal area when environmental variables were included in the model. As monoculture plantations are currently the preferred reforestation method throughout the tropics these results suggest that significant productivity and ecological gains could be made if multi-species plantations are more broadly pursued.

A method for mapping the distribution and density of rabbits and other vertebrate pests in Australia

The European wild rabbit has been considered Australia’s worst vertebrate pest and yet little effort appears to have gone into producing maps of rabbit distribution and density. Mapping the distribution and density of pests is an important step in effective management. A map is essential for estimating the extent of damage caused and for efficiently planning and monitoring the success of pest control operations. This paper describes the use of soil type and point data to prepare a map showing the distribution and density of rabbits in Australia. The potential for the method to be used for mapping other vertebrate pests is explored. The approach used to prepare the map is based on that used for rabbits in Queensland (Berman et al. 1998). An index of rabbit density was determined using the number of Spanish rabbit fleas released per square kilometre for each Soil Map Unit (Atlas of Australian Soils). Spanish rabbit fleas were released into active rabbit warrens at 1606 sites in the early 1990s as an additional vector for myxoma virus and the locations of the releases were recorded using a Global Positioning System (GPS). Releases were predominantly in arid areas but some fleas were released in south east Queensland and the New England Tablelands of New South Wales. The map produced appears to reflect well the distribution and density of rabbits, at least in the areas where Spanish fleas were released. Rabbit pellet counts conducted in 2007 at 54 sites across an area of south east South Australia, south eastern Queensland, and parts of New South Wales (New England Tablelands and south west) in soil Map Units where Spanish fleas were released, provided a preliminary means to ground truth the map. There was a good relationship between mean pellet count score and the index of abundance for soil Map Units. Rabbit pellet counts may allow extension of the map into other parts of Australia where there were no Spanish rabbit fleas released and where there may be no other consistent information on rabbit location and density. The recent Equine Influenza outbreak provided a further test of the value of this mapping method. The distribution and density of domestic horses were mapped to provide estimates of the number of horses in various regions. These estimates were close to the actual numbers of horses subsequently determined from vaccination records and registrations. The soil Map Units are not simply soil types they contain information on landuse and vegetation and the soil classification is relatively localised. These properties make this mapping method useful, not only for rabbits, but also for other species that are not so dependent on soil type for survival.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.